Systems and methods to automatically determine garment fit

ABSTRACT

Systems and methods for automatically determining of garment sizing (e.g., fit) using a images including video images. The method may perform non-contact estimations of garment fit from visual (e.g., video) input by receiving an video of the subject&#39;s head and face and profile and determining a scaling factor from the subject&#39;s intraocular spacing and using this scaling factor when analyzing images of other body regions to determine garment sizing.

CROSS REFERENCE TO RELATED APPLICATIONS

This patent application is a continuation of U.S. patent applicationSer. No. 16/673,915, filed on Nov. 4, 2019, titled “SYSTEMS AND METHODSTO AUTOMATICALLY DETERMINE GARMENT FIT,” which is a continuation of U.S.patent application Ser. No. 15/202,833, filed Jul. 6, 2016, titled“SYSTEMS AND METHODS TO AUTOMATICALLY DETERMINE GARMENT FIT,” now U.S.Pat. No. 10,467,744, which claims the benefit as a continuation-in-partof International Patent Application No. PCT/US2015/010343, filed on Jan.6, 2015 (published as WO 2015/103620), titled “SYSTEMS AND METHODS TOAUTOMATICALLY DETERMINE GARMENT FIT,” which claimed priority to U.S.Provisional Patent Application No. 61/924,086, filed on Jan. 6, 2014,titled “SYSTEMS AND METHODS TO DETERMINE GARMENT FIT” each of which isherein incorporated by reference in its entirety.

INCORPORATION BY REFERENCE

All publications and patent applications mentioned in this specificationare herein incorporated by reference in their entirety to the sameextent as if each individual publication or patent application wasspecifically and individually indicated to be incorporated by reference.

FIELD

The present invention, in some embodiments thereof, relates to methodsand apparatuses (e.g., systems) for determining a subject's measurements(e.g., garment “fit”) using non-contact techniques by examining imagesof the subject. These methods typically generate anthropometricmeasurements of the subject that may be useful for many purposes,including but not limited to assisting in fitting garments or otherwearable devices.

Thus, the invention(s) described herein, in some embodiments thereof,may relate to communication, commerce (including e-commerce), garments,and more particularly to measuring an item or person using an imagecapturing device.

BACKGROUND

There are many instances in which it would be helpful to measure asubject's body remotely, or via non-contact means. In one (non-limiting)example, it would be beneficial to determine a subject's measurements(and therefore garment size(s)) when shopping online, or in othersituation where it is not practical or desired to take conventionalmeasurements. It is highly desirable to determine a garment (e.g.,shirt, shorts, etc.) size which fits a subject well, which can bedifficult when relying on the subject measuring themselves, guessing orrequiring manual assistance.

Although techniques for determining a subject's measurements remotelyhave been proposed by others, such as, for example, U.S. PatentApplication Publication No. 2013/0179288 to Moses et al., such systemsand methods are not accurate or (as in the case with U.S. 2013/0179288),require an external reference object to define a scale and to correctdistortions in the image acquired by the webcam to determine thesubject's measurements. However, external reference items are ofteninconvenient or not available, and may not be properly positioned orselected by the subject.

Described here are apparatuses (e.g., systems and devices, includingcomputer implemented apparatuses) and methods that address many of theseissues. In particular, described herein are apparatuses and methods toevaluate a subject's size on the basis of anthropometric imaging thatcan be easily performed by any user with a PC/smartphone equipped with acamera/webcam. These apparatuses and methods may automatically scale andmeasure the subject, and may thereby determine the subject's sizing(e.g., apparel sizes). The apparatuses and methods described herein donot require any external reference object, but may instead uses one ormore anthropometric parameters, such as interocular distance (IOD), thatcan be determined automatically. The inter-subject variability of IOD isvery low and therefore should introduce an error (<5%) that isacceptable for the purpose.

SUMMARY OF THE DISCLOSURE

In general, described herein are methods and apparatuses forautomatically determining a subject's measurements using one or moreimages of the subject, where at least one image includes the subject'seyes so that an interocular distance can be determined. In any of theapparatuses (e.g., systems) and methods described, the interoculardistance can be used to scale the image(s) so that measurements from theimages can provide calibrated (scaled) measurements of the patient'sbody. These calibrated (scaled) measurements may then be used for anyappropriate purpose, including estimating or otherwise determining asubject's garment size(s). Other purposes may include biometric (e.g.,identity confirmation, etc.) and/or medical monitoring oridentification.

In general, interocular distance may refer to the distance between asubject's eyes, typically measured face-on (e.g., in a frontal image).Interocular distance (IOD) may be interpupillary distance (IPD).Interpupillary distance (IPD) may refer to the distance between thecenters of the pupils of the two eyes, and may help determine the stereoseparation of the two images which are combined in the brain to producestereo perception. Surprisingly, the inter-subject variability of IOD isvery low and therefore should introduce an error (<5%) that isacceptable for the scaling/normalizing purposes described herein.

Although a single average (mean) IOD may be used to calibrate asdescribed herein, in some variations the apparatuses or methods mayselect the appropriate mean IOD based on other factors, including age,race, sex, or the like. In general, any appropriate estimate for meanIPD (IOD) may be used. For example, mean IPD has been quoted in thestereoscopic literature as being from 58 mm to 70 mm, and is known tovary with respect to age, gender and race. According to some literaturevalues (e.g., Dodgson, N. A. (2004). Variation and extrema of humaninterpupillary distance. Proceedings of SHE: Stereoscopic Displays andVirtual Reality Systems XI, Vol. 5291, pp. 36-46), mean adult IPD isaround 63 mm (>17 years old); by gender, the mean is 64.67 mm for menand 62.31 mm for women.

Thus, for example, described herein are methods of automaticallydetermining measurements (e.g., garment sizes) for a subject using acomputing device having a camera, the method comprising: determine thesubject's interocular distance from a frontal image of the subject;determine a scaling factor from the subject's interocular distance;determine measurements for the subject's body from the frontal imageusing the scaling factor; determining a correction function based on thedistance between a plane of the subject's eyes and a plane of asubject's trunk or limbs; and provide an estimate of the subject's bodymeasurements. These body measurement estimates may be used to determinegarment size(s); thus the method may also include providing estimates ofgarment sizes using the measurements.

Any of the methods described herein may be methods of automaticallydetermining garment sizing for a subject using a computing device havinga camera, the method comprising: receiving a frontal image of thesubject including the subject's eyes; determining a scaling factor fromthe subject's interocular distance; scaling the frontal image using thescaling factor; determining measurements of the subject's body from thescaled frontal image; and providing an estimate of the subject's garmentsize using the scaling factor and subject's measurements.

In general, any of the methods described herein may include determininga correction function based on the distance between a plane of thesubject's eyes and one or more other plane of a subject's body; theother plane may be the plane of or tangent to the portion of the bodythat is to be measured (e.g., shoulders, torso/chest, waist, etc.). Thecorrection function (ƒ(x)) may be be a function of the spacing betweenthe plane of the subject's eyes and the other plane (where the distanceis x). The function may be linear (e.g., f(x)=C*x, where C is aconstant, such as a value between 0.1 and 0.95). The function may alsobe based on a surface fitting model, or may be fitted from a curve basedon empirical values. The function may be first order, second order,third order, fourth order, fifth order, sixth order, etc. In somevariations the function may include root function (e.g., square root) ofthe distance between points (d₁ to d₂) on the body and the distance, x,between the planes.

In general, using the scaling factor and correction function maycomprises adding the correction function to the scaling factor (e.g.,scaling in distance units/pixel may be equal to scaling factor+ƒ(x)).The distance units may be any appropriate distance units (matched to theunits of the correction function, such as mm, inches, etc.).

Any of the methods described herein may use more than one image of thesubject. In general, at least one image (a first image) shows thesubject's body including at least the eyes and one other body part(e.g., the head), from which IOD may be determined to determine ascaling factor. Dimensions (measurements) of the other body part (e.g.,head) may then be calculated from the first image and used to scale anyother (e.g., second, third, etc.) images that include at least the oneother body part, by using the calculated dimensions of the (scaled)other body part to scale the other images. The first image may generallybe a frontal image (or at least the front of the face) so that the IODcan be estimated. The additional images, e.g., second image, typicallyshow other angles or views of the subject's body, including the sagittal(side), back, etc.

For example, described herein are methods of automatically determininggarment sizing for a subject using a computing device having a camera;any of these methods may include: receiving a frontal image of thesubject including the subject's eyes and a first body part; determininga scaling factor from the subject's interocular distance; receiving asecond image of the subject including the subject's first body part,wherein the second image is taken from a perspective different than thefirst image; scaling the frontal image including the first body partusing the scaling factor; scaling the second image using a dimension ofthe scaled first body part; determining measurements of the subject'sbody from the scaled second and frontal images; and providing anestimate of the subject's garment size using the measurements.

Any or all of the methods described herein (including some or all ofthese steps) may be performed by a computer processor, e.g.,microprocessor. In particular, these methods may be performed bysoftware, firmware, hardware, or some combination thereof. Any of thesemethods may be performed, for example, as part of an executable(non-transient) program, or “application” that may configure theprocessor of computer, including particularly a mobiletelecommunications device such as a smartphone, tablet (e.g., iPhone™)or the like.

Any of these methods may also include the step of taking the one or more(including the frontal) image of the subject. The method mayautomatically recognize the subject's eyes. Determining the scalingfactor may comprise determining the distance between the centers of thesubject's pupils, the distance between a “center” of the eyes, or thelike.

Any of these methods may also include the step of receiving one or moreof: a subject's age, gender, and race; as mentioned above, theseparameters may further refine the reference IOD used to normalize theimage(s). For example, determining the scaling factor may comprisesusing the subject's interocular distance (IOD) and one or more of thesubject's age, gender, and race, e.g., by selecting a reference IODbased on one or more of the subject's age, race and gender (sex).

Scaling of the second image may comprise using the scaling factor todetermine a size of the first body part from the frontal image andscaling the first body part in the second image using the size of thefirst body part from the frontal image.

Also described herein are non-transitory computer-readable storagemedium storing a set of instructions capable of being executed by acomputing device, that when executed by the computing device causes thecomputing device to determine a subject's body measurements from one ormore images of the subject using the IOD to scale the images.

For example a non-transitory computer-readable storage medium storing aset of instructions capable of being executed by a computing device,that when executed by the computing device causes the computing deviceto determine a subject's interocular distance from a frontal image of asubject that includes the subject's eyes; determine a scaling factorfrom the subject's interocular distance; determine measurements of thesubject's body from the frontal image using the scaling factor; andprovide an estimate of the subject's garment size using themeasurements. As mentioned, the computing device may be a smartphone.The set of instructions, when executed by the computing device, mayfurther cause the computing device to take a frontal image of thesubject and/or additional images of the subject, and/or guide anoperator (e.g., the subject) in taking the appropriate images.

The set of instructions, when executed by the computing device, mayfurther cause the computing device to automatically recognize thesubject's eyes. The set of instructions, when executed by the computingdevice, may further cause the computing device to determine the scalingfactor using the subject's interocular distance and one or more of thesubject's age, gender, and race.

In some variations, a non-transitory computer-readable storage mediumstoring a set of instructions capable of being executed by a computingdevice, that when executed by the computing device causes the computingdevice to: determine a subject's interocular distance from a frontalimage of a subject that includes the subject's eyes; determine a scalingfactor from the subject's interocular distance; determine a scaleddimension of a first body part from the frontal image of the subject andthe scaling factor; scale a second image of the subject using the scaleddimension of the first body part; determine measurements of thesubject's body from the frontal image using the scaling factor and thescaled second image; and provide an estimate of the subject's garmentsize using the measurements.

For example, described herein are methods of automatically determininggarment sizing for a subject from a video of the subject, the methodscomprising: determining the subject's interocular distance from afrontal image of the subject in the video; determining a scaling factorfrom the subject's interocular distance; using the scaling factor todetermine measurements for the subject's body from a plurality of imagesof the subject's body extracted from the video; and providing anestimate of the subject's measurements appropriate for garment sizing.

Any of these method may include: receiving a video of the subject,wherein the video includes at least one frontal image of the subjectincluding the subject's eyes, frontal images of a portion of thesubject's body to be fitted, and side images of the subject's head andportion of the subject's body to be fitted; determining a scaling factorfrom the subject's interocular distance; using the scaling factor toscale the images of the subject's body; determining measurements of thesubject's body from the scaled images; and providing an estimate of agarment size using the subject's measurements.

For example, a method of automatically determining garment sizing for asubject from a video of the subject may include: receiving a video ofthe subject, wherein the video includes at least one frontal image ofthe subject including the subject's eyes, and a plurality of images ofthe portion of the subject's body to be fitted, including frontal andside images; determining a scaling factor from the subject's interoculardistance to convert image space measurements to distance measurements;measuring the portion of the subject's body to be fitted from the video;scaling the measurements using the scaling factor; and providing anestimate of the subject's measurements appropriate for garment sizingusing the measurements of the portion of the body to be fitted.

Any of these methods may include automatically recognizing the subject'seyes. Determining the scaling factor may include determining thedistance between the centers of the subject's pupils. In some variationsthe pupillary size (distance) may also or alternatively be used.

Any of the methods described herein may also include asking and/orreceiving one or more of: a subject's age, gender, and race; further,one or more of age, gender and race may be used to estimate the scalingfactor based on interpupilary distance (interocular distance) byselecting a value for the subject's actual interpupilary distance basedon published values linked values within an age, gender and/or racematched group. For example, determining the scaling factor may generallycomprise using the subject's interocular distance and one or more of thesubject's age, gender, and race.

In any of the methods described herein, the video may comprise acontinuous video scanning the subject's body including frontal andsagittal regions. As used herein a continuous video means a video thatis taken without interruption, so that each frame is separated from eachother by a predetermined time unit.

Providing an estimate of the subject's measurements appropriate forgarment sizing may include providing measurements for one or more of:shoulder length, arm length, arm circumference, neck circumference,upper torso circumference, lower torso circumference, wristcircumference, waist circumference, hip circumference, inseam, and thighmeasurement, calve measurement, etc.

Any of the methods described herein may also include providing a garmentadapted to fit the subject's estimated measurements. The garment may bea stretch fabric (e.g., compression fabric) garment. In some variations,the garment may include one or more electrical elements, such as sensorsand other chips, wires, or the like. Thus, any of these methods may alsoinclude determining a location for one or more biosensors to beintegrated into the garment using the subject's measurements. Sensorsmay include electrodes, which may be specifically positioned over asubject's muscles (e.g., pectoral, bicep, etc.) for EMG measurements,and/or over the subject's heart in specific (e.g., 12-lead ECG)positions, and/or over the subject's chest (e.g., respiration sensors).

Thus, also described herein are methods of automatically determininggarment sizing and positions for one or more biosensors on the garmentfrom a video of a subject, the method comprising: determining thesubject's interocular distance from a frontal image of the subject inthe video; determining a scaling factor from the subject's interoculardistance; using the scaling factor and a plurality of images of thesubject's torso from the video taken at different angles to determinemeasurements for the subject's torso; and providing measurements for acompression garment to be worn by the subject using the measurements ofthe subject's torso, and indicating the locations for one or morebiosensor to be integrated into the compression garment.

Thus, providing the measurements may comprise indicating the locationsof a plurality of ECG electrodes to be integrated into the compressiongarment. In some variations, providing the measurements comprisesindicating the locations of a plurality of respiration sensors to beintegrated into the compression garment.

Also described herein are apparatuses for performing any of the methodsdescribed herein. For example, an apparatus may include software,hardware or firmware configured to control a device (e.g., a hand-helddevice such as a smartphone, tablet, laptop or the like) to perform anyof the functions described herein. In some variations, a non-transitorycomputer-readable storage medium storing a set of instructions capableof being executed by a computing device, that when executed by thecomputing device causes the computing device to: determine the subject'sinterocular distance from a frontal image of a subject in a video;determine a scaling factor from the subject's interocular distance; usethe scaling factor and a plurality of images of the subject's bodyextracted from the video to determine measurements for the subject'sbody; and provide an estimate of the subject's measurements appropriatefor garment sizing.

The set of instructions, when executed by a computing device, may causethe computing device to provide garment sizing information for acompression garment having one or more biosensors integrated therein.For example, the biosensor may comprise a plurality of ECG electrodes,and/or a plurality of respiration sensors.

The set of instructions, when executed by the computing device, mayfurther cause the computing device to automatically recognize thesubject's eyes, and/or determine the scaling factor using the subject'sinterocular distance and one or more of the subject's age, gender, andrace, as discussed above. The set of instructions may further cause thecomputing device to use the scaling factor and a plurality of frontaland sagittal images from the video of a portion of the subject's body todetermine measurements for the subject's body.

Also described herein are non-transitory computer-readable storagemedium storing a set of instructions capable of being executed by acomputing device, that when executed by the computing device causes thecomputing device to: determine the subject's interocular distance from afrontal image of a subject in a continuous video of the subject's headand body including at frontal and sagittal views; determine a scalingfactor from the subject's interocular distance; use the scaling factorand a plurality of images of the subject's body extracted from the videoto determine measurements for the subject's body; and provide anestimate of the subject's measurements appropriate for garment sizing ofa compression garment including a biosensor sensor.

Any of the apparatuses or methods described herein may be configured toautomatically (e.g., without additional human intervention) transmit themeasurements determined directly to a fabrication device for manufactureof the garment, along with identifying information (e.g., name address,etc. for delivery). For example, a fabrication device may include afabric cutter (e.g., a laser machine that will cut the fabric), arobotic device (robot) that may position the components, andparticularly electronic components (e.g., sensors, wires, pcb, etc.), ora 3D printer that will produce the garment. In some variations themeasurements may be encoded as manufacturing-device readableinstructions for manufacture.

In addition, any of the methods described herein may including steps oftaking the video and/or guiding the user to take the video. For example,as described in greater detail herein, a method may include a step ofinstructing the subject how to position the camera for taking the video.The methods and apparatuses may also include reviewing (after the videois taken or while it is taken) the video to confirm that there aresufficient views to take accurate measurements.

Any of the steps described herein may be performed remotely, e.g., by aremote server. For example, any of the steps may be performed by aremote server that analyzes the video. Because the analysis andcalculation of the scaling factor, as well as the steps for determiningmeasurements are processor-intensive, and my therefore requireprocessing time and power that exceeds currently available mobiledevices (e.g., smartphones), these steps may be performed remotely or ina distributed manner.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 is a schematic overview of a method of determining a sizing forgarments, and particularly garments including one or more biosensors.

FIG. 2 is a schematic of one variation of a method for determining asubject's measurements using a frontal image to determine interoculardistance (IOD).

FIGS. 3A and 3B illustrate variations of compression garments includingbiosensors that may be automatically fitted using any of the methods andapparatuses described herein. FIG. 3A shows a garment including aplurality of ECG sensors and FIG. 3B shows a garment including aplurality of respiration sensors.

FIGS. 3C and 3D show front and back views, respectively, of anothervariation of a garment including a plurality of respiration sensors thatmay be automatically fitted using any of the methods and apparatusesdescribed herein.

FIGS. 4A-4E illustrate front profiles (top) and side profiles (bottom)of subject's; in this example, all of the subjects have a similar frontprofile, but very different side profiles, which may result in similarIOD measurements and scaling factors, but may mis-represent sizinginformation based on these scaling factors, without further correction.

FIGS. 5A and 5B illustrate the problem of subject's having similarfrontal profiles and despite having very different side profiles, as thedistance between the IOD front plane and the target body region (e.g.,waist, shoulder, stomach, etc.) may be dramatically different.

FIGS. 6A and 6B illustrate an example of a test garment (including fixedgrid pattern) that may be used to determine or confirm the correctivefunction used with a scaling factor.

DETAILED DESCRIPTION

Described herein are apparatuses and methods for non-contact (andremote) measurements of a subject's body that are automatically andscaled without the necessity of an external (non-intrinsic) reference.Specifically, described herein are apparatuses, including methods anddevices, that use interocular distance to automatically scale one ormore images to measure the dimensions of a subject's body to providesizing information for garments (e.g., clothing).

For example, described herein are method and apparatuses (includingsystems and devices) to calculate precise body measurements of apotential customer in order to ensure best possible fit of apparelcomponents (e.g., shirts, shorts, thighs, gloves, socks, hats,balaclavas, etc.) or the optimal location on the subject's body for adevice and/or garment (e.g., a collar or other component). Inparticular, described herein are methods of automatically determiningbody measurements in order to provide a fitted garment (and particularlya fitted compression garment including one or more electronic biosensors) to a subject.

In general, these methods may use a video of the user that includesimages of the users head (including the eyes) and at least the portionof the body onto which the garment will be worn (e.g., from the waist tothe neck for shirts, from the waist down for pants, etc.). The video maybe taken as a single (continuous) video of the subject, including thefront views, at least one side, and optionally the back (e.g., in amirror or directly). The video may be taken by the user herself/himself,or it may be taken by a third party. The video may be taken, forexample, using a smartphone.

In some variations the apparatus described herein may include anapplication (e.g. software or firmware) for controlling a computingdevice, such as a handheld computing device including a smartphone,tablet, etc. that includes a camera. The apparatus (e.g., application)may guide the user in taking the video, may pre-review the video toconfirm it is adequate, may edit the video to remove unwanted portions,and may alert the user if the video is not sufficient.

Typically, the system automatically recognizes the two medial canthi ofeach eye and calculates their distance in pixels from the image showingthe eyes (e.g., a frontal image). This measured distance may betransformed or correlated from pixels into known units of length (e.g.,mm, inches, etc.) on the basis of known mean anthropometric parameters,such as interocular distance (IOD). In this manner, the systems andmethods do not need any outside reference object in order tocalibrate/scale the images. Because the inter-subject variability of IODis very low (see, e.g., Dodgson, N. A. (2004). Variation and extrema ofhuman interpupillary distance. Proceedings of SPIE: StereoscopicDisplays and Virtual Reality Systems XI, Vol. 5291, pp. 36-46; andSmith, G., & Atchison, D. A. (1997). The eye and visual opticalinstruments. Cambridge UK: Cambridge University Press) the use of apredetermined reference IOD such as 64.67 mm for males and 62.31 mm forfemales, may be sufficiently accurate, particularly for sizing garments,and may introduce little error (<5%).

Thereafter, the conversion factor (which may be referred to as aprinciple conversion factor or an IOD conversion factor may be used forall of the images in the video to covert pixels (virtual distance) toactual measurements (in distance units, such as mm, inches, etc.); whenswitching between the video images, the method (or any apparatusimplementing the method) may also generate and/or use a secondconversion factor for adjusting between video images (e.g., as thecamera is moved, etc.); the second conversion factor may be used tonormalize the pixel sizes between images, and then the primary or IODconversion factor may be applied as well. By using a continuous video(e.g., taking uninterrupted video) this may be made conceptually easier.In addition, the use of continuous video may allow for virtual rotationof the individual to accurately project the subject's torso onto anormal measurement space which may reduce or eliminate errors due toviewing angle of the video images.

Thus, in general the images taken herein may be taken by one or more ofa photo/video-camera on a smartphone, a photocamera, a videocamera, awebcam, or the like. In the examples described herein a video camera isused rather than a photo camera. The use of video allows the apparatusto easily determine the sequence from one frame to the next in terms oftime and position between one frame to the previous or the next one.This is less reliable when performed from a sequence of non-video photossince it is difficult to calculate the time and position distance from ashot to the next. Video may also allow determination of complexmeasurements over highly contoured body regions (e.g., that enabling theapparatus and method to fit a shirt or pair of tights).

In any of the methods described herein, the video should generallyinclude at least one (though multiple are preferred) image of a frontalview including the eyes and a lateral view including the head. Inaddition, it is helpful to provide continuous frames of imagingtransitioning between these images, as well as multiple images of thebody region to be measured (e.g., torso, for shirts, etc.) from multipledifferent angles. From these information, the calculated the size ratio(calibration factor) in mm/pixel may be determined.

Thus, the same images may also show all the body segments that areneeded to be determined in order to take all the sizes, e.g. the widthof the shoulder, the length of the arms, the width of the trunk, thewidth of the neck, etc. These images may then be used to projectmeasurements of these body regions based on the video, and theconversion factor (IOD conversion factor) may allow these virtualmeasurements to be converted into real units (mm, inches, etc.). Thismay allow the method and apparatuses using these methods to correctlyfit shirts, tights or other types of garments rather than simpleaccessories such as glasses, bracelets, watches, necklaces, belts, etc.Thus, although garments such as shirts and parts complex (andsubstantially more complex than jewelry and accessories such as glasses)because they cover a much wider part of the body and because there aremore variances from person to person (for example a shirt could fit twodifference persons as far as shoulders and arms measurements but not instomach or chest dimensions that could present extreme variations), themethods described herein may be used to accurately determine sizing.Furthermore computerized sizing/fitting of compression garments and/orgarments including electrical/electronic components such as sensorspresent added challenges to fitting of traditional clothes because oftheir enhanced functionalities: for example, they may include manysensors to gather physiological data, which may need to touch the skinwhere the signal is strongest. In some variations, a garment includingECG sensors (e.g., ECG electrodes) must be correctly placed near theheart a complex area since it presents substantial variances due todifferent sizes and positions in men's pectorals or women's breasts.Those sensors also need to function mostly in movement thus they need tobe positioned in a way that they can continue to record reliable dataeven when changing position because of body parts movements. Inaddition, while traditional apparel are made of ‘soft’ material such asfabric, cotton, wool, etc. “smart” garments including integratedelectronics may include also ‘hard’ materials such as wires, microchips,connectors, PCB, etc. or other ‘hard’ components that are notcomfortable to wear. Thus to minimize discomfort it is important tolocate and properly measure those parts of the body where those ‘hard’materials/parts should best be located, as described herein.

One challenge in automatically determining sizing is in preparing thecorrect and appropriate input images. For example, one challenge of sucha system that may automatically measure a body region for a garment isthat the images should be easily taken by the user himself or herself,without the need for complex equipment, such as dedicated instruments totake body size measurements. Described herein are simple methods forperforming these automatic measurements that may be based on deviceswhich may be generally available to most individuals (e.g.,general-purpose smartphone, photo/video cameras, webcam, etc.). Further,as another parameter, the methods descried herein may be completelyautomatized methods, which do not require any user intervention and thatprovides all final measurements in a completely automatic manner.

In general, these methods may be used to measure for garments thatnormally cover parts of the body that are traditionally difficult tomeasure virtually, such as shirts (upper body), hoodies (upper body andhead), slacks and pants (lower body including thighs, buttocks, etc.);gloves (wrists and hands), socks (ankles and feet), balaclava (neck andhead), etc.

In addition, the video methods described herein may also be used tomeasure parts of the body in movement. These methods and apparatuses maycalculate the measurements to maximize the fit and the comfort of thegarment, and may filter the measurements so that they can maximize fitand comfort while accepting users fashion desires.

Finally, these method and apparatuses are particularly helpful forconfiguring and fitting so-called ‘smart’ garments which may electroniccomponents integrated into the garment, including one or more sensors(e.g., “biosensors”). For example, these methods and apparatuses may, inaddition to determining fit, determine the correct sensors positioningafter defining the garment measurements based on the body dimensions.Some sensors, such as ECG and EMG electrodes, must be preciselypositioned in specific parts of the body in order to acquire ameaningful physiological signal. For example, pairs of EMG electrodesshould be precisely placed on each muscle, to avoid noise coming fromother close muscles. Similarly, ECG electrodes should not be placed onmuscles such as pectorals in order to avoid the EMG noise that couldoverride the ECG waves. Positioning of these sensors may therefore beimportant (e.g., for skin conductance sensing, sensors may be locatedfrom arm pits to latissamus muscles, while for EMGs, sensors may bepositioned near the center of biceps).

In any of the variations described herein, the video of the subject'sbody may be taken so that it includes at least one image in which theeyes (in frontal images) and the head (in lateral images) are clearlyframed. In addition, it would be useful to take video including theseviews and stay in a position which is at sufficiently ‘frontal’ or‘lateral’ with respect to the sensor of the photo/video-camera, and toallow the photo/video camera to frame all the body segments which areneeded to customize the garment, and specifically multiple views of thebody regions over which the garment is to be worn (e.g., to customize ashirt, it is needed to have all the trunk, the arms and the neck).

In general, any of the apparatuses and methods described herein may beconfigured to take images of the head, including the face and in somevariations the side of the head, to determine a scaling factor, but thesame video may also provide images of the body regions, generally thetrunk and/or limbs, that are being fitted automatically as describedherein. The subject's trunk may generally refer to the person's bodyapart from the limbs and head, and may specifically include the chest,shoulders, abdomen, back, waist, hips, crotch region, buttocks, etc. Thelimbs typically include the arms and legs.

For example, any of the method and apparatuses described herein mayinclude taking the video and/or instructing the user (subject) in a wayto take the video to acquire the images used. As mentioned above, insome variations instructions may be provided in which the user isinstructed to take a video to have, in at least one image, andpreferably more images, the information described above (e.g., frontalviews including the face and eyes, and body region to be fitted,transitioning to/from side/sagittal views including the head and bodyregions to be fitted.

In some variations the method, or an apparatus for performing themethod, may include instructing a subject to take the video themselves.As mentioned, the subject may be instructed to take the video eitherwithout clothing over the region to be fitted, or in tight fittingand/or minimal clothing. For example, for measuring the torso, thesubject may be instructed to remove any loose upper body clothing (e.g.ideally they should be nude and/or wearing only tight underwear or abra, alternatively, wearing a tight compression shirts and compressiontights, or less optimally, wearing a tight shirt and tight pants). Forprivacy sake, the video may be encrypted to prevent viewing by thirdparties, and the user may be provided information indicatingconfidentiality. In some variations the system is configured so that thevideo is erased or otherwise destroyed after determining measurements.In some variations the video may be modified before transmittingremotely.

For example, in some variations the video may be analyzed locally (e.g.on a handheld device such as a smartphone) to determine the interoculardistance and a scaling factor before transmitting the rest of the video,including the body (e.g., all or a portion of the truck and/or limbs) toa remote server for later analysis; however the video may be modified toremove the subject's head and/or face, or to obscure the subject's headand/or face, prior to transmitting the video, e.g., to a remote serverfor analysis. In this example, the scaling factor and/or the interoculardistance may be indicated on one or more frames of the video so thatbody measurements generated from the video can be properly scaled.

In one variation, the subject may be instructed to perform a series ofmovements to capture a continuous video with the images useful for themethods described herein. As mentioned above, in some variation theapparatus may talk the user through this process, for example, providingaudible guidance to the user as they hold the video device and take theimages.

In one variation the subject may be instructed to hold the video camera(e.g., phone camera, etc.) with two hands in front of them (in order tohave even shoulders position, rather than holding the video camera withonly one hand), with their arms extended as forward or as far aspossible. This may allow them to film a larger part of the body, and mayinclude the head and face, neck, shoulders, and upper body, includingdown to the belly region. The subject may tilt the video camera (e.g.,phone) to capture the face and body in the video. The subject may beinstructed to hold the video camera as parallel to the body as possible,for between 1-5 seconds (e.g., 3 seconds). The subject may then beinstructed to hold the video camera in the right arm (e.g., straight outfrom the body), and lift the left arm from the side of the body and upas straight as possible to be parallel to the body, and held for 1-5seconds (e.g., 3 seconds). Next, the user may be instructed to take thevideo camera in their left hand and hold the camera out from the bodyand move their right arm, raising the right arm from the side of thebody up to a position straight out and parallel from the body (and heldfor 1-5, e.g., 3, seconds). The subject may then be instructed to lowerthe right arm and rotate the extended the left arm to their side,holding the camera parallel to the floor and in the same plane as thefront of the torso to film the left side of the head, and then in acontinuous movement bend your elbow to touch the trunk so as to film theleft side of the head and the left shoulder. This step may be repeatedwith the camera a in the other hand to film the right side of the headand of the right shoulder. The subject may then bet told to bring theright arm (holding the video camera) in front of the body to take holdof the video camera with both hands again to return to the initialposition and hold for the appropriate time (e.g., 1-5 seconds, such as 3seconds). The user may then be instructed to, while preserving videocamera position parallel to the body, lower it to record rest of thefront lower-trunk to include upper legs (and hold for 1-5, e.g., 3seconds). Users may also be instructed to stand with their back facing amirror and to take a 3 seconds video of their back of the body: head,shoulders, upper trunk and lower trunk all the way down to back of upperlegs. The total video typically takes no more than 20 seconds. Movementsshould be as steady and continuous as possible. To facilitate theoperation users can play a tutorial video from the smartphone (e.g., ifusing an application on the smartphone) or be guided to a websiteproviding a guide of the movements).

In some variations the user may work with a third party to take theimages. The images may be similar to those taken as described above,except that user may start in a “crucifix” position, with arms asextended as possible, then rotate the arms form the side to over thehead, lower the arms along the body and rotate the entire body 90° tothe left for a video of the left side of the body, and further rotatethe body to the left by 90° to be taken a video of the back. From theback, both arms may be lifted into the ‘crucifix’ position and then thearms may be lifted in an extended parallel position over the head, thehands may be lowered along the body, and the body may be rotated by 90°to the left for a video of the right side of the body.

Other movements for imaging the body either by a third party or by theuser alone (and/or in front of a mirror) may be used. Generally, it isimportant that as much of the region of the body to be covered by thegarment be imaged in the video as smoothly as possible, without stopping(introducing discontinuities in the video). In some variations theapparatus may detect problems with the video (e.g. focus, magnification,lighting levels, etc.) or may perform some image processing (e.g.,detecting body position, separation of the body from background, etc.)and may instruct the subject to adjust or re-take the video accordingly.

The video may then be transmitted to a remote server (e.g., over aninternet connection) for automated analysis, and/or analyzed locally(e.g., on the smartphone or computer). In some variations the apparatusmay include one or more functions to allow automatic uploading of thevideo, including securing the transmission (e.g., by encryption, etc.).In some variations the video may be analyzed to determine the qualityprior to transmission, so that the subject may be instructed to takeanother image. Quality may be improved by using high resolution cameras,using more frames to calculate an average size ratio (mm/pixels) insteadof a single image, and/or by automatic detection and/or recognition ofbody features (face, eyes, head, torso, etc.) to confirm the videoincludes sufficient views. In general, the subject may be provided withinstructions in order to improve image acquisition.

As used herein, a server may refer to an application (e.g., software,firmware, etc.) capable of accepting requests from a client and givingresponses accordingly. Servers can run on any computer, includingdedicated computers, which individually are also often referred to as“the server”. A computer can have several servers runningsimultaneously. The server maybe run on a dedicated computer. Clientdevices (e.g., remote devices) may connect to a server through a networkbut may run on the same computer. In the context of Internet Protocol(IP) networking, a server is a program that operates as a socketlistener.

In any of the variations described herein, the user may also provide theapparatus with additional information (e.g., gender, height, weight,etc.), which may be used by the method to refine the analysis, includingthe determination of a scaling factor from the interocular distance.

The video images may be filtered by digital filters in order to enhancethe contrast between the body and the background, and/or to eliminateimage noise. These methods may also allow the user to acquire multipleimages from multiple points of views. Measurements obtained throughvideo may be filtered through existing libraries of body measurements tofurther refine the measurements.

When additional information (e.g., height, weight, gender, etc.) areincluded, this information about the user may help to improve themeasuring process. For example, weight, height and age can help thesystem to pre-assign the user to a specific anthropometric measurementscluster, in order to filter outliers and false positive given byexternal sources of noise that could affect the measurements (e.g. lowlight, blur).

In general, the program requires just one tool on the user's side: adevice that can record video, handle basic video processing and getaccess to the Internet. For instance this device could be represented bythe user smartphone. FIG. 1 illustrates one example of a process flowthe functionalities described herein. For example, a method ofautomatically determining a subject's measurements for garment includingwearable electronics, the method may include preparing the video (e.g.,preparing the subject to take the video), including checking the initialvideo set-up 101. The apparatus (e.g., an application running on asmartphone) may be configured to perform this step initially, checkingthe video camera settings against predefined preferences for taking thevideo, and alerting the user if they need to adjust and/or automaticallyadjusting them. The apparatus may also provide instructions and/orguidance on what movements should be done to record the video. Onceprepared, the video may be taken 105. The video may be vetted eitherduring recording or after recording 107 to determine that it issufficient for the detection, as mentioned above. Once it passes, it maybe uploaded to a remote server for processing 109, 111. This generallyinclude determining the interocular distance and measuring the bodyregions to fit a garment (e.g., for a shirt, measuring neck, arm length,shoulder width, upper torso, lower torso, torso length, etc.).Measurement may be made by rendering/projecting the body region ofinterest and using this virtual/reconstructed body to determine lengths.These measurements may then be converted to actual length measurementsusing the interocular distance based on the standard interoculardistance parameter values and particular the intraocular distance valuesspecific to the gender and/or age of the subject.

Video processing may be performed in parts, for example, normalizing thevideo images to be used to each other and in particular to the image(s)used to determine the scaling factor from the interocular distance,and/or projecting or calculating surface dimensions providingmeasurements of the subject's body. For example, the video processing111 may include determining dimensions of the surface of the subject'sbody (e.g., by modeling and/or reconstructing a model of the subjectfrom the video images), and then using the dimensions and the scalingfactor (or alternatively, but scaling the model and/or images formingthe model first, so that the dimensions are already expressed in thecorrect units) to determine a measurement for the body in real-worldunits 117 for length or areas (e.g., inches, cm, etc.). In somevariations the methods and apparatuses for using them may alternativelyconvert these measurements into garment sizes, including standard orcustom sizing units. As described in detail herein, any of the methodsand apparatuses for performing them described herein may optionallyinclude defining optimal positioning for electrical components 119 usedin wearable electronics, such as sensors (e.g., electrodes, etc.),wiring (e.g., conductive traces, inks, etc.), processing elements(chips, circuits, processors, etc.), and connectors (multi-pinconnectors, etc.).

Further, any of the methods and apparatuses described herein maytransmit the measurements directly to a fabrication device 121. Forexample, any of these methods and/or systems may be connected orconnectable (including directly connected or connectable) to one or morefabrication devices, such as 3D printers, laser cutters, sewingmachines, etc.

In variations in which sensors will be positioned on the body, thesensor positions may be located onto the device in predeterminedlocations relative to body landmarks (e.g., pectoral regions, etc.). Forexample, FIGS. 3A-3B illustrate one variation of a compression garmentthat includes a series of sensors, including sensors specific to ECGmeasurements. A dual series of ECG electrodes 303 may be positioned onthe chest in the predetermined regions typically corresponding to thelead positions (e.g., 12 lead ECG positions). FIG. 3A shows a front viewand FIG. 3B shows a back view of a garment including a shirt and pantsthat may be connected to each other. The location of the sensors on thechest (and writs, shoulder, ankles, etc.) may be precisely determined inan individualized and customized way using the methods and apparatusesdescribed herein.

Similarly, FIGS. 3C and 3D shows front and back views, respectively, ofa shirt having respiration sensors 319 around the torso. The garment 303is a compression garment, and may include additional sensors 333, 335.The methods and apparatuses described herein may be used to preciselyand customizable locate the position of the sensors on the body so thatrespiration may be accurately determined.

As described herein the main processing stage used to determine the bodymeasurements and calculate the garment dimensions may be handled by aserver-based program. Thus, this may be done after recording the video.The server-based program may process the video without any additionalrequirement for the subject and may ensure a cross-platformcompatibility (because the core processing will not be dependent bydifferent OS and hardware architectures). However in some variations,the processing may be done at least in part, if not entirely, locally(e.g., in the smartphone, laptop, desktop, etc.).

Further, even if processing of the video is done remotely, some basicchecking and calculations may be performed in real-time by the part ofthe program responsible for recording the video, thus on the toolrequired on the user side, as described above. For example, theapparatus may tell the subject if the setup (e.g. environment light,image quality, blur) is suitable or not for this application. In caseany parameter does not fulfill the expected requirements, the programmay give instruction to the users on how to improve the setup.

After automatically checking environment parameters, the user may beallowed or instructed to start recording the video by following one ofthe procedure above. To facilitate the operations, users may be able toplay a tutorial video that will guide them through all the necessaryvideo steps. During the whole recording phase, other processing stagesmay be performed by the apparatus. For example, the apparatus mayimplement a face (and head) recognition feature to help the users tocorrectly acquire the video. In addition, this may also continuouslycheck for some recording parameters such as blur or video stability.These parameters could affect the server-based processing, thus theusers may be notified in case one of them will exceeds the expectedranges.

Once the video recording is complete, the video may be uploaded to aremote server, where it will may processed to determine the bodymeasurements and determinate the garments sizes following the stepsshown in FIG. 2, described below. The server-based processing mayintegrate the measurements with anthropometric database in order tofilter any outliers and false positives.

When fitting for garments including electronics (e.g., wearable sensorsand/or electronics), once the garment sizes are determined, the programmay continue to the last processing phase in which the garmentdimensions and the body measurement are used to define model that willbe used to determine the optimal sensors positioning.

FIG. 2 is a flowchart that schematically illustrates one methods ofdetermining a subject's measurements (and therefore sizes) using IOD toscale/normalize images of the subject. For example, in FIG. 2, thesubject stands first facing the camera of their PCs or smartphones nudeor wearing compression apparel or just tight undergarment (underwear,bra, etc.). The system can then automatically calculates his/hercorporeal measurements without the need of a reference object byrescaling the image based on the distance between the subject's eyes.Additional views of the subject may be used. For example, the subjectcan stand in profile (on a side) in order to allow the calculation ofadditional dimensions in the sagittal view, such as antero-posteriordiameters of the abdomen, of the chest, of the breast, etc. Thesefurther calculations will be based on another scaling factor calculatedby referring to dimensions of body parts that are present in both thefrontal and sagittal view (e.g. the height of the head).

Any of the apparatuses (e.g., systems) and methods for performingnon-contact automatic estimation of garment fit from visual (e.g. videoor pictures) input may include input of at least: a subject's head andface, front profile, side profile and back profile. In some variations,these images (e.g., head and face, front profile, side profile and backprofile) may be sufficient, although additional images (includingoverlapping images) may be used. In general the methods and apparatusesdescribed herein may not require any external reference object (e.g.,having a known dimension, such as a coin, credit card, etc.), butinstead uses anthropometric parameters as a reference for measurementsinstead; in particular, the interocular distance (IOD), which can bedetermined automatically, may be used as a starting reference distancefor the entire measuring process. The inter-subject variability of IODis very low and therefore should introduce an error (<5%) that isacceptable for the purpose.

In any of the variations described herein, the system and apparatus mayalso determine a correction factor or scaling factor to correct for thespacing between different frontal body planes (e.g., different depth offield), based on an estimate of the distance between the frontal planes(e.g., a plane of the face, from which IOD may be estimated, and a planeof the gut and/or torso). The separation between these planes may beestimated from one or more profile images taken perpendicular to thefrontal planes.

For example, as discussed above, the apparatus (e.g., system) or methodmay first estimate the intraocular distance analyzing a picture (orvideo frame) in which the subject's head and face are placed right infront of the camera. The system or method may then (e.g., automaticallyor manually) recognize the subject's eyes and calculate the IOD inpixels. Knowing the average IOD in millimeters, a scaling factor (e.g.,mm/pixel) may then be calculated as discussed and illustrated above.This scaling factor may then be used as a reference to estimate otheranthropometric distances (e.g. the height/width of the head) that can bealso used as a reference distance for other measurements.

After analyzing a picture of the subject's head and face, the method (ora system implementing it) may also analyze other pictures in which atleast the subject front profile, side profile and back profile areshown. Considering that different anthropometric distances have alreadybeen estimated (e.g. the height/width of the head) they can be used as areference distance to calculate new scaling factors when analyzingpictures of the subject front profile, side profile and back profile.For instance, the width of the head (which may be calculated inmillimeters during the analysis of the head and face based on theIOD-determined scaling factor) may be used to calculate a scaling factorfor analyzing the back profile. This new scaling factor may then be usedto estimate the width of the shoulders and other anthropometricdistances within the subject's back profile. Even though the system isable to calculate and use different reference distances (e.g. IOD, widthor height of the head, etc.) to determine a series of translationalscaling factors between different images, these measurements often referto parts of the body placed on different planes (e.g., different frontalplanes) or at different depth of field. As mentioned, an additionalcorrective or correctional factor based on the separation betweendifferent depth of field may be used.

For instance, when analyzing a front profile picture, distances measuredon the front plane that includes (e.g., virtually cuts) the eyes may bein a different plane that the plane tangent to the stomach, thus causingadditional and unwanted errors into the distances measurement whenestimating size of these out-of-plane portions of the body. See, forexample, FIGS. 4A to 4E, illustrating different frontal planes. FIG. 4A,top, shows a frontal image of a subject, showing the subject's face andbody, including the IOD spacing; FIG. 4A, bottom, shows a side profile,illustrating the distance between the facial (IOD) plane 401 and thesubject's belly plane 403; this spacing is not apparent in the differentfrontal images shown in FIGS. 4B-4E, top, which otherwise look quitesimilar to FIG. 4A, top. As illustrated in FIGS. 5A and 5B, withoutcorrecting for the different spacing between these planes, the scalingfactors used (based on the IOD and/or other landmarks, such as headsize, arm/leg/hand/finger length, shoulder width, etc.) may lead toinaccuracies. In FIG. 5A, the frontal images appear nearly identical,and the scaling factors, without correction for the spacing between thedifferent frontal planes, will be off. When determining waist and/orchest measurements for fitting a garment, as described herein, this maylead to a significant error. As illustrated in FIG. 5B, the scalingfactor may be corrected by adding a correcting function (ƒ(x)) based onthe spacing (x) between the plane in which the scaling factor wasdetermined (e.g., IOD facial plane 501) and the plane tangent to thebody region 503, 503′ for which a measurement is to be estimated, suchas the torso/chest or waist (in FIG. 5B the waist is shown).

In general, this function may be a linear (e.g., first order), secondorder, third order, fourth order, etc., function. For example, thefunction (ƒ(x)) may be expressed generally as a function of the distancebetween the planes, in (mm or pixels) and/or a function of the distancebetween the point(s) in the plane (e.g., the stomach plane) beingdetermined. In general the function ƒ(x) may return a correcting valuethat is less than the distance between the planes.

Thus, to compensate, the method or apparatus may determine and apply acorrection algorithm that compensates the different depths of fields bycombining the analysis performed during the whole process. For instancefrom the analysis of subject's side profile, the system implementing themethod may estimate the distance between the IOD plane and the stomachplane, calculating the correction factor to be applied when performingthe measurements of subject front profile.

The methods (e.g., algorithms) described above may be constantlyimproved for accuracy and performances through machine learning andusing any of the apparatuses (systems and devices) described herein. Forexample, an apparatus as described herein may include an upper-body anda lower-body garment with a grid design of known dimensions, which maybe used to help establish and/or modify the correctional factors (f(x))used herein. For example a grid garment such as the one shown in FIGS.6A and 6B may be sent to subject's and their actual measurement andmeasurements estimated using IOD as described herein may be made, andcompared to improve the accuracy of estimates. The use of such deviceshas proved that the general methods (e.g., algorithms) described hereinare correct, and the grid design is used by a machine learning systemsto ensure that all the anthropometric distances and scaling factors areaccurately estimated.

Terminology used herein is for the purpose of describing particularembodiments only and is not intended to be limiting of the invention.For example, as used herein, the singular forms “a”, “an” and “the” areintended to include the plural forms as well, unless the context clearlyindicates otherwise. It will be further understood that the terms“comprises” and/or “comprising,” when used in this specification,specify the presence of stated features, steps, operations, elements,and/or components, but do not preclude the presence or addition of oneor more other features, steps, operations, elements, components, and/orgroups thereof. As used herein, the term “and/or” includes any and allcombinations of one or more of the associated listed items and may beabbreviated as “/”.

Spatially relative terms, such as “under”, “below”, “lower”, “over”,“upper” and the like, may be used herein for ease of description todescribe one element or feature's relationship to another element(s) orfeature(s) as illustrated in the figures. It will be understood that thespatially relative terms are intended to encompass differentorientations of the device in use or operation in addition to theorientation depicted in the figures. For example, if a device in thefigures is inverted, elements described as “under” or “beneath” otherelements or features would then be oriented “over” the other elements orfeatures. Thus, the exemplary term “under” can encompass both anorientation of over and under. The device may be otherwise oriented(rotated 90 degrees or at other orientations) and the spatially relativedescriptors used herein interpreted accordingly. Similarly, the terms“upwardly”, “downwardly”, “vertical”, “horizontal” and the like are usedherein for the purpose of explanation only unless specifically indicatedotherwise.

Although the terms “first” and “second” may be used herein to describevarious features/elements, these features/elements should not be limitedby these terms, unless the context indicates otherwise. These terms maybe used to distinguish one feature/element from another feature/element.Thus, a first feature/element discussed below could be termed a secondfeature/element, and similarly, a second feature/element discussed belowcould be termed a first feature/element without departing from theteachings of the present invention.

As used herein in the specification and claims, including as used in theexamples and unless otherwise expressly specified, all numbers may beread as if prefaced by the word “about” or “approximately,” even if theterm does not expressly appear. The phrase “about” or “approximately”may be used when describing magnitude and/or position to indicate thatthe value and/or position described is within a reasonable expectedrange of values and/or positions. For example, a numeric value may havea value that is +/−0.1% of the stated value (or range of values), +/−1%of the stated value (or range of values), +/−2% of the stated value (orrange of values), +/−5% of the stated value (or range of values), +/−10%of the stated value (or range of values), etc. Any numerical rangerecited herein is intended to include all sub-ranges subsumed therein.

Although various illustrative embodiments are described above, any of anumber of changes may be made to various embodiments without departingfrom the scope of the invention as described by the claims. For example,the order in which various described method steps are performed mayoften be changed in alternative embodiments, and in other alternativeembodiments one or more method steps may be skipped altogether. Optionalfeatures of various device and system embodiments may be included insome embodiments and not in others. Therefore, the foregoing descriptionis provided primarily for exemplary purposes and should not beinterpreted to limit the scope of the invention as it is set forth inthe claims.

The examples and illustrations included herein show, by way ofillustration and not of limitation, specific embodiments in which thesubject matter may be practiced. As mentioned, other embodiments may beutilized and derived there from, such that structural and logicalsubstitutions and changes may be made without departing from the scopeof this disclosure. Such embodiments of the inventive subject matter maybe referred to herein individually or collectively by the term“invention” merely for convenience and without intending to voluntarilylimit the scope of this application to any single invention or inventiveconcept, if more than one is, in fact, disclosed. Thus, althoughspecific embodiments have been illustrated and described herein, anyarrangement calculated to achieve the same purpose may be substitutedfor the specific embodiments shown. This disclosure is intended to coverany and all adaptations or variations of various embodiments.Combinations of the above embodiments, and other embodiments notspecifically described herein, will be apparent to those of skill in theart upon reviewing the above description.

What is claimed is:
 1. A method of automatically determining garmentsizing for a subject from a video of the subject, the method comprising:receiving a video of the subject, wherein the video includes at leastone frontal image of the subject including the subject's eyes, and aplurality of images of the portion of the subject's body to be fitted,including frontal and side images; determining a scaling factor from thesubject's interocular distance to convert image space measurements todistance measurements; measuring the portion of the subject's body to befitted from the video; scaling the measurements using the scalingfactor; and providing an estimate of the subject's measurementsappropriate for garment sizing using the measurements of the portion ofthe body to be fitted.
 2. The method of claim 1, further comprisingautomatically recognizing the subject's eyes.
 3. The method of claim 1,wherein determining the scaling factor comprises determining thedistance between the centers of the subject's pupils.
 4. The method ofclaim 1, further comprising receiving one or more of: a subject's age,gender, and race.
 5. The method of claim 1, wherein determining thescaling factor comprises using the subject's interocular distance andone or more of the subject's age, gender, and race.
 6. The method ofclaim 1, wherein the video comprises a continuous video scanning thesubject's body including frontal and sagittal regions.
 7. The method ofclaim 1, wherein providing an estimate of the subject's measurementsappropriate for garment sizing comprises providing a shoulder, armmeasurement, neck, upper torso, and lower torso measurement.
 8. Themethod of claim 1, further comprising providing a garment adapted to fitthe subject's estimated measurements.
 9. The method of claim 1, furthercomprising determining a location for one or more biosensors to beintegrated into a garment using the subject's measurements.
 10. Themethod of claim 1, further comprising automatically transmitting thesubject's measurements to a machine configured to fabricate the garment.11. The method of claim 1, further comprising determining a correctionfunction based on a distance between a plane including the subject'seyes and a plane of the subject's trunk or limbs.
 12. The method ofclaim 11, wherein using the scaling factor and correction functioncomprises adding the correction function to the scaling factor todetermine a number of distance units per pixel.
 13. A method ofautomatically determining garment sizing for a subject, the methodcomprising: receiving a video of the subject, wherein the video includesat least one frontal image of the subject including the subject's eyes,and a plurality of images of the portion of the subject's body to befitted, including frontal and side images; determining a scaling factorfrom the subject's interocular distance to convert image spacemeasurements to distance measurements; measuring the portion of thesubject's body to be fitted from the video, wherein measuring isperformed without reliance upon an external reference object; scalingimage measurements of the portion of the subject's body using thescaling factor to obtain distance measurements of the portion of thesubject's body; and providing an estimate of the subject's measurementsappropriate for garment sizing.
 14. A non-contact method ofautomatically determining garment sizing for a subject, the methodcomprising: receiving a video of the subject, wherein the video includesat least one frontal image of the subject including the subject's eyes,and a plurality of images of the portion of the subject's body to befitted, including frontal and side images; determining a scaling factorfrom the subject's interocular distance to convert image spacemeasurements to distance measurements; measuring the portion of thesubject's body to be fitted, wherein measuring is performed by examiningthe video, thereby providing image space measurements of the portion ofthe subject's body without contacting the subject; scaling themeasurements using the scaling factor, thereby providing distance spacemeasurements of the portion of the subject's body; and providing anestimate of the subject's measurements appropriate for garment sizing,wherein providing comprises delivering the distance space measurementsof the portion of the subject's body.
 15. The method of claim 14,wherein determining the scaling factor comprises determining thedistance between the centers of the subject's pupils.
 16. The method ofclaim 14, further comprising receiving one or more of: a subject's age,gender, and race.
 17. The method of claim 14, wherein determining thescaling factor comprises using the subject's interocular distance andone or more of the subject's age, gender, and race.
 18. The method ofclaim 14, wherein the video comprises a continuous video scanning thesubject's body including frontal and sagittal regions.
 19. The method ofclaim 14, further comprising determining a correction function based ona distance between a plane including the subject's eyes and a plane ofthe subject's trunk or limbs.
 20. The method of claim 19, wherein usingthe scaling factor and correction function comprises adding thecorrection function to the scaling factor to determine a number ofdistance units per pixel.